The impact of outliers on Cronbach's coefficient [alpha] has not been documented in the psychometric or statistical literature. This is an important gap because coefficient [alpha] is the most widely used measurement statistic in all of the social, educational, and health sciences. The impact of outliers on coefficient [alpha] is investigated for varying values of population reliability and sample sizes for visual analogue scales. Results show that coefficient [alpha] is not affected by symmetric outlier contamination, whereas asymmetric outliers artificially inflate the estimates of coefficient [alpha]. Coefficient [alpha] estimates are upwardly biased and more variable sample to sample, with increasing asymmetry and proportion of outlier contamination in the population. However, these effects of outliers on the bias and sample variability of coefficient [alpha] estimates are reduced for increasing population reliability. The results are discussed in the context of providing guidance for computing or interpreting coefficient [alpha] for visual analogue scales. (Contains 1 table and 3 figures.)